Learning salience amoung [sic] features through contingency in the CEL framework

Determining which features in an environment are salient given a task, salience assignment, is a central problem in Machine Learning. A related phenomenon, contingency (the conditions under which relative salience among environmental features is acquired), is central to learning and memory in animal psychology. This paper presents an analysis of a set of empirical data on contingency and an algorithm for the salience assignment problem. The algorithm presented is implemented in a working computer program which interacts with a simulated environment to produce contingent associative learning corresponding to relevant behavioral data. The model also makes specific empirical predictions that can be experimentally tested

GENERALIZED HEDGE RATIO ESTIMATION WITH AN UNKNOWN MODEL

Myers and Thompson (1989) pioneered the concept of a generalized approach to estimating hedge ratios, pointing out that the model specification could have a large impact on the hedge ratio estimated. While a huge empirical literature exists on estimating hedge ratios, the literature is lacking a formal treatment of model specification uncertainty. This research accomplishes that task by taking a Bayesian approach to hedge ratio estimation, where specification uncertainty is explicitly modeled. Specifically, we present a Bayesian approach to hedge ratio estimation that integrates over model specification uncertainty, yielding an optimal hedge ratio estimator that is robust to possible model specification because it is an average across a set of hedge ratios conditional on di erent models. Model specifications vary by exogenous variables (such as exports, stocks, and interest rates) and lag lengths included. The methodology is applied to data on corn and soybeans and results show the potential benefits and insights gained from such an approach.Marketing,

Generalized Hedge Ratio Estimation with an Unknown Model

Myers and Thompson (1989) noted that the model specification could have a large impact on the hedge ratio estimated. A huge literature exists on estimating hedge ratios, but the literature is lacking a formal treatment of model specification uncertainty. This research accomplishes that task by taking a Bayesian approach to hedge ratio estimation, where specification uncertainty is explicitly modeled. The methodology is applied to data on hedging of corn and soybeans and on cross-hedging of corn oil using soybean oil futures. Results show the potential benefits and insights gained from such an approach.Marketing,

Prison-Based Education and Re-Entry into the Mainstream Labor Market

We estimate the post-release economic effects of participation in prison-based General Educational Development (GED) programs using a panel of earnings records and a rich set of individual information from administrative data in the state of Florida. Fixed effects estimates of the impact of participating in the GED education program show post-release quarterly earnings gains of about 15 percent for program participants relative to observationally similar non-participants. We also show, however, that these earnings gains accrue only to racial/ethnic minority offenders and any GED-related earnings gains for this group seem to fade in the third year after release from prison. Estimates comparing offenders who obtained a GED to those who participated in GED-related prison education programs but left prison without a GED show no systematic evidence of an independent impact of the credential itself on post-release quarterly earnings.

Prison-Based Education and Re-Entry into the Mainstream Labor Market

incarceration, GED, earnings

A structural empirical model of firm growth, learning, and survival

In this paper we develop an empirical model of entrepreneurs' business continuation decisions, and we estimate its parameters using a new panel of monthly alcohol tax returns from bars in the state of Texas. In our data, entrepreneurial failure is frequent and predictable. In the first year of life, 20% of our sample's bars exit, and these tend to be smaller than average. In the model, an entrepreneur bases her business continuation decision on potentially noisy signals of her bar's future profits. The presence of noise implies that she should make her decision based on both current and past realizations of the signal. We observe for each bar its sales, which we assume, equals a noisy version of the entrepreneur's signal. That is, the entrepreneur's information about her bar is private. ; The entrepreneur's private information makes the estimation of our model challenging, because we cannot observe the inputs into her decision process. Nevertheless, we are able to recover from our observations the parameters characterizing the entrepreneur's learning process and the noise contaminating publicly available sales observations. The key to our analysis is to note that our ability to forecast the entrepreneur's decisions reveals the amount of noise contaminating publicly available sales observations. We infer that public and private information differ little if we can forecast entrepreneurs' business continuation decisions well. With this information, we can then determine whether the usefulness of past sales observations for forecasting future sales arises only from the noise contaminating public observations or if the observations imply the presence of additional noise contaminating entrepreneurs' observations. ; We estimate our model using observations from the first twelve months of life for approximately 300 Texas bars. We find that entrepreneurs observe the persistent component of profit without error. In this sense, their information is substantially superior to the public's.Business enterprises ; Corporations

Oligopoly dynamics with barriers to entry

This paper considers the effects of raising the cost of entry for potential competitors on infinite-horizon Markov- perfect industry dynamics with ongoing demand uncertainty. All entrants serving the model industry incur sunk costs, and exit avoids future fixed costs. We focus on the unique equilibrium with last- in first-out expectations: a firm never exits before a younger rival does. When an industry can support at most two firms, we prove that raising barriers to a second producer’s entry increases the probability that some firm will serve the industry and decreases its long-run entry and exit rates. In numerical examples with more than two firms, imposing a barrier to entry stabilizes industry structure.Oligopolies

Precise Predictions for Higgs Production in Neutralino Decays in the Complex MSSM

Complete one-loop results are obtained for the class of processes chi^0_i->chi^0_j h_a in the MSSM where all parameters entering this process beyond lowest order are allowed to have arbitrary CP-violating phases. An on-shell renormalisation scheme is worked out for the chargino-neutralino sector that properly takes account of imaginary parts arising from complex parameters and from absorptive parts of loop integrals. The genuine vertex contributions to the neutralino decay amplitudes are combined with two-loop propagator-type corrections for the outgoing Higgs boson. In this way the currently most precise prediction for this class of processes is obtained. The numerical impact of the genuine vertex corrections is studied for several examples of CP-conserving and CP-violating scenarios. We find that significant effects on the decay widths and branching ratios are possible even in the CP-conserving MSSM. In the CP-violating CPX benchmark scenario the corrections to the decay width are found to be particularly large, namely, of order 45% for a Higgs mass of 40 GeV. This parameter region of the CPX scenario where a very light Higgs boson is unexcluded by present data is analysed in detail. We find that in this parameter region, which will be difficult to cover by standard Higgs search channels at the LHC, the branching ratio for the decay chi^0_2->chi^0_1 h_1 is large. This may offer good prospects to detect such a light Higgs boson in cascade decays of supersymmetric particles.Comment: 24 pages, 6 figure

A Structural Empirical Model of Firm Growth, Learning, and Survival

We present a structural model of firm growth, learning, and survival and consider its identification and estimation. In the model, entrepreneurs have private and possibly error-ridden observations of persistent and transitory shocks to profit. We demonstrate that the model's parameters can be recovered from public observations of sales and survival, and we estimate them using monthly data from new bars in Texas. We find that entrepreneurs observe profit's persistent component without error. In this sense, their information is substantially superior to the public's.